LV Prasad Eye Institute Final Presentation

Transcription

1 LV Prasad Eye Institute Final Presentation Ali Kamil, Dmitriy Lyan, Nicole Yap, MIT Student MIT Sloan School of Management Global Health Lab May 8, Courtesy of Ali S. Kamil, Dmitriy E. Lyan, Nicole Yap, and MIT Student. Used with permission.

2 Agenda About LVPEI Opportunity, challenges, and approach General observations Analysis and recommendations Next steps Appendix 2

3 LVPEI is a non-profit organization focused on the delivery of eye care to patients at all levels of the economic pyramid. Hyderabad Campus Services offered: Comprehensive patient care Clinical research Sight enhancement and rehabilitation Community eye health Education Product development Centre of Excellence: Provides outpatient services to 200,000 people Performs 25,000 surgeries Trains 250 professionals at all levels of eye care Provides low vision services to 3,000 people LVPEI Eye Health Pyramid LV Prasad Eye Institute. All rights reserved. This content is excluded from our Creative Commons license. For more information, see 3

4 The GHL team at MIT Sloan was engaged to identify bottlenecks and causes of high patient service time in the LVPEI outpatient department (OPD). Challenges High patient service times High provider fatigue due to high patient volume and extended hour of service Opportunities Reduce service time without compromising LVPEI s high standard of quality care Increase capacity without compromising LVPEI s high standard of quality care Team Approach Pre-trip (January to March) On-site (Mid- to late- March) Post-trip (April to May) 4 Engaged Sashi Mohan, Head of Operations, and Raja Narayan, Head of Clinical Services at LVPEI, over Skype We interviewed key personnel at hospitals in the Boston area, including operations leaders at Massachusetts General Hospital (MGH) and practitioners at Massachusetts Eye and Ear Infirmary (MEEI) and Mount Auburn Hospital Conducted time and motion studies in two cornea and two retina OPD clinics, collecting timestamps on the flow of patient folders, and noting management practices Interviewed faculty ophthalmologists and optometrists, and OPD scheduling administrator Conducted patient surveys at the walk-in counter Ran statistical analyses to quantitatively identify relationships between different variables and patient service times Compared findings to our interviews and observations of management practices Derived recommendations for addressing systemic causes of increases in patient service times in the OPD

5 5 GENERAL OBSERVATIONS

6 Patient pathways varied significantly depending on the clinic, and on the type of patient and appointment. Check-In Work-up Consultation Investigation Check-out Retina Start Dilation Work-up 30 minutes Retina Consultation Yes Yes Retina Investigation Investigation required? No Investigations units are subject to their own process flow management practices. End Cornea Dilation required? Yes No Cornea Consultation Cornea Investigation Dilation 30 minutes 6

7 Additionally, several combinations of factors impact patient service time. Hospital-Specific Factors Commitment to training medical staff Patient volume vs.. hospital capacity Scheduling-Specific Factors Doctor-specified appointment and walk-in templates Administrator's adherence to doctor-specified appointment templates Real-time prioritization of patients Clinic-Specific Factors Management of patient folders and staff # of Fellows, Optometrists, and Facilitators Skill levels of staff Size and layout of clinics Anticipated vs.. actual patient volume Types and variety of patients that can be seen Need for diagnostics Patient-Specific Factors Lack of awareness of appointment-based system Bias for early morning arrival High volume of late arrivals and no shows 7

8 There is little discrepancy in patient service time between nonpaying and general patients, but high variability ranging from two to four hours. Priority Level Patient Count Clinic 1 Clinic 2 Clinic 3 Clinic 4 Average Service Time G 182 2:44 4:08 2:17 3:26 3:12 NP 56 2:55 3:35 2:51 4:01 3:29 Average patient service times for general and nonpaying patients differed by only 17 minutes. 9:36 8:24 Mean (μ) 3h 15m SD (σ) 1h 37m Service Time Variability (All Days) Service Time (h:mm) 7:12 6:00 4:48 3:36 2:24 1:12 σ μ -σ 8 0:

9 Walk-in patients have higher variability in service times compared to patients with appointments. Clinic 1 Clinic 2 Clinic 3 Clinic 4 6:21 5:01 4:38 5: Walk-in patient service time is higher than aptbased patients Average Service Time Total Patient Count 5: % of walk-ins arrive before 12pm Walk-in Patient Arrival Time vs.. Service Time Walk-in Patient Service Time Variability (all days) 10:36 9:24 Mean (μ) 5h 4m SD (σ) 2h 3m 8:12 7:00 5:48 4:36 3:24 2:12 σ μ -σ 9 1:

10 ANALYSIS & RECOMMENDATIONS 10

11 Require doctors to adhere to appointment based system and encourage on-time arrivals. Key Observations Only 28% of all apt. based patients arrived on time Clinics adhering to apt. based system achieved shorter service times Clinics 1 and 3 adhered to apt. based system and penalized patients for early (<30m) or late arrivals (>30m) Clinics 2 and 4 did not actively adhere to apt. system and had significantly higher service times for on-time patients Service Times for On-time Patients Clinic 1 Clinic 2 Clinic 3 Clinic 4 Avg. Service Time 2h 22 m 3h 58m 2h 4m 3h 38m St. Dev 1h 17 m 1h 33m 1h 22m 1h 23m Recommendations Require doctors to adhere to apt. based system Prioritize patients based on their appointment times and not check-in times Educate patients about apt. based system and encourage adherence :06 2:50 3:35 3:31 3:38 4:10 3:10 2: >4 hours early 3: :00 3:47 3 hours 2 hours early early 1 hour early 3:09 Arrival Time 2:22 On-time 30min window 1 hour late 3:21 2:27 Service Time 0:00 2 hours 3 hours late late Clinic 1 Clinic 4 4> hours late 2:06 2:23 4:19 3:50 3:21 2:52 2:24 1:55 1:26 0:57 0:28 0:00 4:48 4:19 3:50 3:21 2:52 2:24 1:55 1:26 0:57 0:28 0:00 11

12 Encourage the use of appointment system, while simultaneously employing strategies to better manage walk-in patients. Walk-in Survey Results Summary 40 patients surveyed in total 41% of patients had tried unsuccessfully to make an appointment; 50% of these were because the requested appointment time was unavailable 80% of patients who did not make appointments were unaware of the option Survey Findings In general, awareness of the appointment option is low Patients choose the walk-in option because the next available appointment is too far away The majority of walk-in patients are new to LVPEI Interview Findings Doctor scheduling for walk-in patients by time and type is often not adhered to due to over demand and incorrect triage Unexpected walk-ins are disruptive to the patient flow, but doctors have no choice but to accommodate Incorrect triage results in re-routing patients to other clinics and increased service time Walk-in patients often have primary care concerns that do not require specialized attention, or ask to see a specific doctor unnecessarily 12 Ideas to Consider Better promote appointment system, especially among new patients Designate general doctors for walk-in clinic to reduce specialist time on general cases Require referral letters for new patients asking to see a specific doctor Enforce ophthalmologist-set guidelines for appointment booking at the walk-in counter

13 Identify factors contributing to decreasing service times in the late afternoon. # of Patients : :52 Apt. Based Patients Arrivals & Service Times (all clinics) 3:57 3:36 3:23 3:29 3:28 3:01 2:35 2: :06 1 4:19 3:50 3:21 2:52 2:24 1:55 1:34 1:26 0:57 0:28 0:00 Average Service Time (8am 1pm) 3h 30m Key Observations Average service time decreases with time of day Appointment-based patients arrival time has normal distribution Average Service Time (1pm 7pm) 2h 30m Potential Factors to Consider Providers work more efficiently towards the end of the day Patients that do not require diagnostics are stacked later in the day Reduced number of walk-ins in the latter half of the day Ideas to Consider Closely observe the behavioral patterns of providers during the later half of the day. If positive behavior is identified, this practice should be replicated during the rest of the day. 13

14 Monitor practitioner fatigue in latter half of workday, as high pressure to serve customers can lead to increased errors and reduced service quality. Interview Findings Error rate of providers rises throughout the day for both optometrists and ophthalmologists After 4:00PM, doctors begin to observe fatigue in their teams After 4:00PM, doctors begin to observe work being completed in a hurry Key Observations Patients who arrive later in the day and patients who arrive significantly late for their appointments tend to experience lower service times. Average service time decreases with time of day With time of day, providers and staff tend to get fatigue and are prone to mistakes/errors 4:22 3:53 3:24 2:55 2:26 1:58 1:29 1:00 Apt. Based Patients Arrivals & Service Times (all clinics) Service Pressure vs.. Time per Patient Time per Patient Ideas to Consider Closely observe the error rate that is created at any given time Closely observe the frequency of re-work over a given time period Determine the cause of the decline in service times during latter half of the day 10am 1pm Time of Day 4pm 14

15 Monitor practitioner fatigue in latter half of workday, as high pressure to serve customers can lead to increased errors and reduced service quality. Insights Workday is scheduled for 8am 5:30pm. Providers observed working until 7/8pm to service all patients High patient backlog increases pressure on LVPEI providers to service all patients in a given day Latter part of the day has been observed (via interviews) to increased fatigue and errors in service High pressure situation coupled with long workdays will lead to high turnover of staff Apt. Based Patients Target Wait Time Per Patient Time Remaining in Workday Current Wait Time Per Patient based on Backlog Walkin Patients Patient Arrival Rate - Required Service Time - - Service Pressure B Pressure Buildup Standard Workday LVPEI Patient Backlog Workday Total LVPEI Staff - B Reduced Time Per Task Time Per Patient Patient Check-out Rate - Actual Service Time R Errors Created Error Fraction - <Workday> Rate of Change in Perception about Wait Times - Impact of Fatigue on Cross Consultations Burnout Rate Impact of Fatigue on Error Fraction Fatigue/Burnout Perception of Long Wait Times at LVPEI Perception Buildup Time Avg. Workday for Past Month Fatigue Buildup time Ideas to Consider Adherence to apt. based system and reducing number of walk-in patients Consider provider/staff rotation between highpressure clinics and regular clinics Identify rework and errors created by time of day Modeling Next Steps Consider long term impact to quality of service and reputation due to high service times and errors/rework Identify impacts to staffing and turnover due to high pressure environment Consider competitor/alternate emergence scenario 15

16 Identify and encourage best cross-consultation management practices Relevant Clinic Observations Cross-consultation cases comprise a non-negligible percentage in each clinic: 10 to 15% 3 out of 4 clinics employed practices to manage and integrate cross-consultation cases into existing patient flow Management of cross-consultation cases differed across clinics Passive cross-consultation management was disruptive to regular patient flow Sample Cross-Consultation Management Practices Fixed time allocation: 15 minutes every 2 hours for cross-consultations and short follow-ups Real-time prioritization: integration and prioritization of cross-consultations with existing patients Prioritization by check-in: prioritization of cross-consultation patients according to check-in time Ideas to Consider Conscious management of cross-consultation patients in each clinic Identification of good cross-consultation management practices Closer observation of the decision-making process behind the need for cross-consultation Guidelines for providers on the necessity for cross-consultation 16

17 Remove annual post-surgery follow-up requirement and divert patients to comprehensive clinic for ongoing long follow-ups Observations 60-70% of doctor s appointment templates are dedicated to seeing new patients 20% of all patients seen across the four days of study are new patients. Providers perform over 500 surgeries a year All patients are requested to come back for follow-ups at least once a year regardless of the need. 17 Total Addressable Market Adoption Rate New Patient Arrival Rate New Patient Appointment Slots - LVPEI Non-Surgical Patients LVPEI OPD Patient Backlog NS Patient Dep Rate B Hotel California Patient Service Rt. Follow Up Patient Arrival Rt Surgical Case Fraction LVPEI Post Surgical Surgery Rate Patients Follow Up Appointments SG Patient Dep Rate Insights Continuing with the policy of requiring patients to come back for simple follow ups exhausts LVPEI doctors capacity to serve new patients. Dedicating more of providers time to follow up patients reduces opportunities to learn from diverse and complex cases. Ongoing reduction in time available to see new patients limits LVPEI s ability to realize its vision to reach all those in need. Ideas to Consider Removing the requirement for all patients to come in for yearly follow-ups post-surgery Transitioning fully recovered patients to comprehensive clinic for ongoing long follow-ups

18 18 NEXT STEPS

19 Additional studies and modeling exercises will build a comprehensive understanding of the factors contributing to patient service time in the OPD. Future Studies Time and motion studies that include cross consultation patients Time and motion studies on cornea diagnostics Patient flow of patients before they get to clinics Triage process at the walk-in counter Patients returning to LVPEI due to incorrect diagnosis Effectiveness of short-term recommendations Identification of best practices in clinic management Simulation Models Additional data collection needed to quantify key relationships LVPEI s patient flow system for cornea and retina clinics 19

20 20 QUESTIONS?

21 21 APPENDIX

22 Monitor practitioner fatigue in latter half of workday, as high pressure to serve customers can lead to increased errors and reduced service quality. System Dynamics Service Pressure Loop Apt. Based Patients Walkin Patients Patient Arrival Rate LVPEI Patient Backlog Patient Check-out Rate Rate of Change in Perception about Wait Times - Perception of Long Wait Times at LVPEI Perception Buildup Time Target Wait Time Per Patient Time Remaining in Workday Current Wait Time Per Patient based on Backlog - Required Service Time - - Service Pressure B Pressure Buildup Standard Workday Workday Total LVPEI Staff - B Reduced Time Per Task Time Per Patient - Actual Service Time R Errors Created Error Fraction - <Workday> Impact of Fatigue on Cross Consultations Impact of Fatigue on Error Fraction Fatigue/Burnout Burnout Rate Avg. Workday for Past Month 22 Fatigue Buildup time

23 Summary Of Approximate Patient Waiting Times Day Clinic 1 Clinic 2 Clinic 3 Clinic4 Type Workup Waiting Time Diagnostic Total Service Waiting Time Waiting Time Appointment 1:28 2:33 2:04 Walk In 1:47 3:21 5:16 New 1:54 3:42 3:55 Follow Up 1:26 2:19 2:01 Appointment 2:29 2:40 2:54 Walk In 3:16 1:31 3:42 New 2:27 2:37 4:02 Follow Up 3:32 1:59 2:47 Appointment 1:05 0:46 1:12 Walk In 4:07 3:32 New 1:57 2:07 3:45 Follow Up 1:29 1:39 0:36 Appointment 1:40 0:24 2:41 Walk In 2:37 0:52 4:03 New 3:45 2:09 3:16 Follow Up 2:57 0:18 3:37 Number Of Patients Percentage of Walkins Percentage of New Patients 57 7% 14% 97 30% 26% 72 11% 14% % 22% 23

24 Overall Patient Average Service Time 6:00 4:48 3:36 Service Time 2:24 1:12 0:00 Appointments Walk Ins New Follow Ups 22:48 21:36 Clinic 1 Clinic 2 Clinic 3 Clinic 4 24

25 Overall Patient Check-in to Dilation Average Service Time 4:48 4:19 3:50 Service Time 3:21 2:52 2:24 1:55 1:26 Appointments Walk Ins New Follow Ups 0:57 0:28 0:00 Clinic 1 Clinic 2 Clinic 3 Clinic 4 25

26 Overall Patient Arrival Rates Clinic 1 Clinic 2 Clinic 3 Clinic :00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 26

27 Patient Arrival & Service Completion Rates 14 Arrival and Service Completion Clinic Patients/Hour 8 6 Arrival Rates Service Rates :00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 27

28 Patient Arrival & Service Completion Rates 25 Arrival and Service Completion Clinic 2 20 Patients/Hour Arrival Rates Service Rates 5 0 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 28

29 Patient Arrival & Service Completion Rates 14 Arrival and Service Completion Clinic Patients/Hour 8 6 Arrival Rate Service Rate :00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 29

30 Appointment-based Patient Patient Type (G,NP, S, SS) & Service Time Service Time 3:36 3:28 3:21 3:14 3:07 3:00 2:52 2: : : :02 G NP S SS 5 3: Number of Patients Patient Types Service Times 30

31 Appointment-based Patient Distribution Early/Late/On-Time Arrivals Arrivals (Total) NA 0% On Time 28% Early Arrivals 46% Late Arrivals 26% Clinic 1 NA 0% Clinic 2 NA 0% Clinic 3 NA 0% Clinic 4 NA 0% 31 Late Arrival s 23% On Time 19% Early Arrival s 58% Late Arrivals 15% On Time 30% Early Arrivals 55% On Time 35% Late Arrivals 25% Early Arrivals 40% On Time 25% Late Arrivals 36% Early Arrivals 39%

32 Appointment-based Patient Arrival & Service Time (Clinic 1) : :52 3:47 3: : :21 3:21 2:52 Patient # :10 2:27 2:06 2:24 1:55 Service Time :26 4 0: :28 0 >4 hours early 0 0:00 3 hours early2 hours early 1 hour early On-time 30min window 0 0:00 1 hour late 2 hours late 3 hours late 4> hours late 0:00 32

33 Appointment-based Patient Arrival & Service Time (Clinic 2) 25 6: : :48 3:58 4:05 3:58 4:07 Patient # : :31 3:36 2:24 Service Time :50 5 1:12 0 >4 hours early 3 hours early 2 hours early 1 hour early On-time 30min window 2 1 hour late 2 hours late 0 0:00 3 hours late 1 4> hours late 0:00 33

34 Appointment-based Patient Arrival & Service Time (Clinic 3) 25 6: :00 4:48 Patient # :35 2:50 3:36 2:24 Service Time 2:04 8 1: :31 5 1:17 1: :00 0:00 >4 hours early 3 hours early 2 hours early 1 hour early On-time 30min window 1 hour late 2 hours late 3 hours late 4> hours late 1 2 0:00 34

35 Appointment-based Patient Arrival & Service Time (Clinic 4) : :10 4: :35 3:31 3: :10 3:50 3:21 Patients # :06 2: :44 2:23 2:52 2:24 1:55 Service Time 6 6 1: :57 0:28 0 >4 hours early 3 hours early 2 hours early 1 hour early On-time 30min window 1 hour late 2 hours late 3 hours late 4> hours late 0:00 35

36 Walk-in Patient Arrival & Service Time 10:48 9:36 9: :24 7:35 14 Service Time 7:12 6:00 4:48 6:17 4:10 4:22 4:05 6: Patient # 3:36 2:24 2:37 2: :12 2 0: : : : Walkin Arrival Time vs Srvc Time Service Time 36

37 Appointment-based Patient Arrival vs. Appointment Time Variability 0.50 Difference b/w Check-in and Apt Times (0.10) (0.20) (0.30) Early Late 37 (0.40) Day 1 Day 2 Day 3 Day 4

38 Appointment-based Patient Service Time Variability for On-Time (Clinic 1) 6:00 4:48 Service Time (h:mm) 3:36 2:24 1:12 0:00 9:38 9:54 10:47 11:29 11:29 12:01 12:28 13:11 13:11 14:43 38

39 Appointment-based Patient Service Time Variability for On-Time (Clinic 2) 9:36 8:24 7:12 Service Time (h:mm) 6:00 4:48 3:36 2:24 1:12 0:00 7:40 8:04 8:10 8:54 9:18 9:05 9:48 10:12 10:21 10:38 11:10 11:20 12:41 13:15 13:14 13:12 13:52 14:05 14:35 16:02 39

40 Appointment-based Patient Service Time Variability for On-Time (Clinic 3) 6:00 4:48 Service Time (h:mm) 3:36 2:24 1:12 0:00 9:22 10:07 10:42 10:48 10:49 10:49 11:10 11:27 11:10 11:38 11:36 12:00 12:17 13:25 14:10 14:05 14:18 15:01 15:21 15:44 16:00 16:07 40

41 Appointment-based Patient Service Time Variability for On-Time (Clinic 4) 6:00 4:48 Service Time (h:mm) 3:36 2:24 1:12 0:00 41

42 Team Profiles Ali Kamil is a System Design & Management Fellow at MIT Sloan and MIT School of Engineering. Prior to MIT, he spent 6 years in corporate strategy consulting advising leading entertainment, media, and telecom clients. His research and interests are focused on developing low-cost ICT based innovations for base of pyramid populations in developing and emerging economies. Originally from Pakistan, Ali intends to pursue a career in international development and social entrepreneurship post MIT Dmitriy Lyan is a second year SDM student at MIT, where he is specializing in development of performance management systems for shared value focused organizations. In his thesis work he is using system dynamics methodology to explore performance dynamics in US military behavioral health clinics. Prior to MIT Dmitriy spent 5 years working in investment banking and asset management as well as 2 years in software development industries. He holds an M.S. in Financial Engineering and a B.S. in Computer Engineering. He plans to apply his talents in impact investing and social entrepreneurship. MIT Student is an MSc in Management Studies student at MIT Sloan. Prior to MIT, she spent 5 years in the financial service industry, marketing multi-asset investment solutions to institutional clients. After graduation, MIT Student hopes to employ management skills to disseminate innovative and affordable interventions designed to empower marginalized individuals in sub-saharan Africa. Nicole Yap is an MSc in Management Studies student at MIT Sloan. She has two years of consulting experience, advising large private and public sector clients on their Customer Relationship Management (CRM) strategies. Her research focuses on the development of marketbased policies and approaches that organizations can apply to sustainably reach developing markets. Nicole plans to apply her management consulting background to the development of sustainable global health strategy upon graduation in

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